让自动驾驶懂交通法规，结合视觉与规则库做合法路径规划。
Traffic Regulation-aware Path Planning with Regulation Databases and Vision-Language Models
- 用视觉语言模型解析摄像头画面生成描述文本
- 结合机器可读法规库实现合规路径规划
- 在仿真与实车中验证，适合自动驾驶安全研发
本文提出并测试了一种将交通法规合规性融入自动驾驶系统（ADS）的框架。该框架使系统能够依据驾驶环境遵循交通法规并做出合理决策。通过使用RGB相机输入和视觉语言模型（VLM），系统生成描述性文本以支持法规感知的决策过程，确保合法安全的驾驶行为。这些信息与可机器读取的自动驾驶法规数据库结合，指导未来驾驶路径在法律约束内制定。关键特性包括：1）支持决策的法规数据库；2）基于传感器输入的自动化法规感知路径规划流程；3）在仿真与真实环境中的验证。尤其真实车辆测试不仅评估了框架性能，还考察了VLM在整合检测、推理与规划方面解决复杂驾驶问题的潜力与挑战。本工作提升了自动驾驶的合法性、安全性与公众信任，是该领域的重要进展。
原文摘要 · Abstract (English)
This paper introduces and tests a framework integrating traffic regulation compliance into automated driving systems (ADS). The framework enables ADS to follow traffic laws and make informed decisions based on the driving environment. Using RGB camera inputs and a vision-language model (VLM), the system generates descriptive text to support a regulation-aware decision-making process, ensuring legal and safe driving practices. This information is combined with a machine-readable ADS regulation database to guide future driving plans within legal constraints. Key features include: 1) a regulation database supporting ADS decision-making, 2) an automated process using sensor input for regulation-aware path planning, and 3) validation in both simulated and real-world environments. Particularly, the real-world vehicle tests not only assess the framework's performance but also evaluate the potential and challenges of VLMs to solve complex driving problems by integrating detection, reasoning, and planning. This work enhances the legality, safety, and public trust in ADS, representing a significant step forward in the field.
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